Clinical subjects often have large and rapid movements during a functional magnetic resonance imaging (fMRI) scan session, and both of these types of errors need to be avoided or corrected for a successful analysis. Many current clinical data sets cannot be analyzed because of subject motion during the scan session. Head motion causes failures in fMRI analysis and a great deal of effort has been expended to train subjects to be motionless, or constrain their heads in the scanner via packed pillows, bite bars, or custom head cast. These physical constraints are unpleasant at best, and not possible to use on many young, old, or disturbed subjects of interest.
Methods have been developed to attempt to correct for subject motion without solely relying on physical constraints. The first step in fMRI processing is to realign the data so all the images are registered to each other. Even after realignment and interpolation, there is unavoidable residual time series errors caused by the subject's movements. These errors occur regardless of the interpolation algorithm that is used because the spatial high frequency structure of the brain cannot be adequately captured with any interpolation algorithm using data samples that are spatially far apart. In other words, the errors are inherent due to limited spatial resolution of the data samples compared to brain structures of interest. Techniques using motion regressors in the design matrix during fMRI analysis have been developed to correct for motion correction. However, these existing techniques have not been effective against typical subject motions in high motion clinical data sets. In fact, those techniques are completely unusable for many high motion data sets. Technical papers relating to these techniques include Friston et al., “Movement related effects in fMRI time-series,” Magnetic Resonance in Medicine, Vol. 35 (1996), pp. 346-355 and Grootoonk et al., “Characterization and Correction of Interpolation Effects in the Realignment of fMRI Time Series”, NeuroImage 11, 49-57 (2000). Grootoonk et al. utilizes a principle component analysis (PCA) that approximates the variations in intensity due to subject motion by using two functions: sine and cosine. However, the method of Grootoonk et al. fails to transition smoothly in the limit of low subject motion. This failure is unavoidable due to the functions that naturally arise as a consequence of the PCA in the algorithm of Grootoonk et al. Furthermore, the method of Grootoonk et al. is based on faulty assumptions by addressing the issue of poor interpolation algorithms and fails to appreciate the issue that actual fine-grain brain structure makes even perfect interpolation algorithms fail.
Existing techniques for motion correction in fMRI also suffer from difficulties with regularization. In particular, many existing techniques are over-regularized, causing errors for the corrected motion. Current fMRI motion correction algorithms also rely on poorly aligned images, thereby introducing unreliable results. In addition, many existing algorithms require many reads of the images to be corrected, which is computationally and time inefficient.